Quick Demo

Sachin Yadav, IIT Gandhinagar, sachin.yadav@iitgn.ac.in

IRIS Flower data set

The data set contains information about three species of IRIS flowers namely:

• Iris setosa
• Iris versicolor
• Iris virginica

Four features are collected from each sample, sepal-length, sepal-width, petal-length and petal-width in centi-meters.

# Common imports
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

import plotly.express as px

iris_df = pd.read_csv("./data/IRIS.csv.gz", compression="gzip")
iris_df.sample(5)

	Unnamed: 0	sepal_length	sepal_width	petal_length	petal_width	species
144	144	6.7	3.3	5.7	2.5	Iris-virginica
48	48	5.3	3.7	1.5	0.2	Iris-setosa
52	52	6.9	3.1	4.9	1.5	Iris-versicolor
23	23	5.1	3.3	1.7	0.5	Iris-setosa
78	78	6.0	2.9	4.5	1.5	Iris-versicolor

fig = px.scatter_3d(iris_df, x='sepal_length', y='sepal_width', z='petal_width',
              color='species', template="plotly_dark")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

COVID-19 Dataset

covid_df = pd.read_csv("./data/owid-covid-data.csv.gz", compression="gzip")
covid_df.sample(5)

	Unnamed: 0	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	...	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
10853	10853	AZE	Asia	Azerbaijan	2021-05-24	332610.0	156.0	334.429	4867.0	7.0	...	0.3	42.5	83.241	4.70	73.00	0.756	NaN	NaN	NaN	NaN
100174	100174	MCO	Europe	Monaco	2021-03-14	2107.0	1.0	12.000	27.0	0.0	...	NaN	NaN	NaN	13.80	86.75	NaN	NaN	NaN	NaN	NaN
106753	106753	NLD	Europe	Netherlands	2020-06-14	49025.0	165.0	172.714	6074.0	2.0	...	24.4	27.3	NaN	3.32	82.28	0.944	6192.8	8.28	-0.81	360.610616
57792	57792	DEU	Europe	Germany	2021-01-18	2059382.0	9253.0	16895.143	47263.0	362.0	...	28.2	33.1	NaN	8.00	81.33	0.947	NaN	NaN	NaN	NaN
127086	127086	SPM	North America	Saint Pierre and Miquelon	2020-05-16	1.0	0.0	0.000	NaN	NaN	...	NaN	NaN	NaN	NaN	81.07	NaN	NaN	NaN	NaN	NaN

5 rows × 68 columns

required_columns = ["iso_code", "location", "continent", "date", "new_cases_smoothed", "total_cases"]
covid_df = covid_df.dropna(subset = required_columns)

covid_df = covid_df.sort_values("date")

covid_df[['iso_code', 'location']].sample(8)

	iso_code	location
108579	NZL	New Zealand
44672	EGY	Egypt
113531	NOR	Norway
160272	VUT	Vanuatu
28832	CYM	Cayman Islands
5829	ATG	Antigua and Barbuda
162045	VNM	Vietnam
51780	FLK	Falkland Islands

Covid-19 New Cases Worldwide on 23 December 2021

covid_day_df = covid_df[covid_df.date == "2021-12-23"]
covid_day_df.sample(5)

	Unnamed: 0	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	...	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
6568	6568	ARG	South America	Argentina	2021-12-23	5428957.0	13456.0	7473.571	116979.0	15.0	...	16.2	27.7	NaN	5.00	76.67	0.845	NaN	NaN	NaN	NaN
79511	79511	KAZ	Asia	Kazakhstan	2021-12-23	1068899.0	536.0	480.143	18160.0	15.0	...	7.0	43.1	98.999	6.70	73.60	0.825	NaN	NaN	NaN	NaN
164594	164594	YEM	Asia	Yemen	2021-12-23	10105.0	2.0	2.714	1981.0	0.0	...	7.6	29.2	49.542	0.70	66.12	0.470	NaN	NaN	NaN	NaN
75445	75445	ISR	Asia	Israel	2021-12-23	1360912.0	1745.0	1090.143	8241.0	2.0	...	15.4	35.4	NaN	2.99	82.97	0.919	NaN	NaN	NaN	NaN
16183	16183	BLZ	North America	Belize	2021-12-23	31444.0	57.0	32.429	592.0	1.0	...	NaN	NaN	90.083	1.30	74.62	0.716	NaN	NaN	NaN	NaN

5 rows × 68 columns

fig = px.scatter_geo(covid_day_df, locations="iso_code", color="continent",
                     hover_name="location", size="new_cases_smoothed",
                     projection="natural earth", template="plotly_dark")

fig.show()

Covid-19 Total Cases over the days

fig = px.scatter_geo(covid_df, locations="iso_code", color="continent",
                     hover_name="location", size="total_cases",
                     projection="natural earth", animation_frame="date", template="plotly_dark")

fig.show()

MNIST Dataset

import torchvision
import os

import matplotlib.pyplot as plt
from matplotlib import rc
from matplotlib.animation import FuncAnimation
from matplotlib import animation

rc('animation', html='jshtml')

frn = 10 # Number of frames to process in the animation
fps = 0.5 # Frames per second
mywriter = animation.PillowWriter(fps=fps) 

mnist_dataset = torchvision.datasets.MNIST(root = "data/mnist", train = True, download = True, transform=torchvision.transforms.ToTensor())

fig, ax = plt.subplots(figsize = (10, 10))

def change_plot(frame_idx):
    ax.cla()
    image_tensor = mnist_dataset[frame_idx][0]
    image_tensor_gray = image_tensor[0]
    image_tensor_gray = image_tensor_gray * 255
    ax.matshow(image_tensor_gray, cmap = "gray")
    for i in range(image_tensor_gray.shape[0]):
        for j in range(image_tensor_gray.shape[1]):
            ax.text(i, j, str(int(image_tensor_gray[j][i].item())), va = "center", ha = "center", color = "blue", fontsize = "small")
    ax.axis("off")
    plt.tight_layout()

anim = FuncAnimation(fig, change_plot, frn, interval=1000 / fps)
plt.close()
anim

Once Loop Reflect

<Figure size 432x288 with 0 Axes>

mywriter = animation.PillowWriter(fps=fps)

if not os.path.exists("./assets/gif"):
    os.makedir("./assets/gif")
    
anim.save('./assets/gif/mnist.gif',writer=mywriter)

<Figure size 432x288 with 0 Axes>